The below detection limit issue is similar to survival analysis with 
censoring (but left rather than right censoring).   So many survival 
estimation approaches are thus appropriate for analyses with below 
detection limits (see NADA package, also censored quantile regression in 
quantreg package, etc).

Brian

Brian S. Cade, PhD

U. S. Geological Survey
Fort Collins Science Center
2150 Centre Ave., Bldg. C
Fort Collins, CO  80526-8818

email:  brian_cade@usgs.gov
tel:  970 226-9326



From:
Bert Gunter <gunter.berton@gene.com>
To:
Jessica Streicher <j.streicher@micromata.de>
Cc:
r-help@r-project.org
Date:
08/13/2012 09:28 AM
Subject:
Re: [R] Imputing data below detection limit
Sent by:
r-help-bounces@r-project.org



Yes, Jessica, the practice -- of which I also have been and continue
to be guilty -- does not really make a lot of sense. It usually
doesn't affect estimation all that much, but it can certainly mess up
inference. The proper approach is to use the proper approach: model it
as left-censored data. The problem with that is:

1. It's complicated, and is beyond the statistical background of most
folks who deal with such data -- it's a ubiquitous issue in science
and engineering after all.

2. Typically, the LOD isn't: that is, there often is not a well
defined value and that which is chosen is both arbitrary and
inaccurate. What one often sees is an increasing loss of relative
precision as one "approaches" the designated value. Modeling this
effectively gets even more complicated. David Rocke and colleagues has
published methodology on this, mostly in TECHNOMETRICS if memory
serves.

3. So, as in other situations, we muddle along with rather crude
statistical approaches and hope that they are adequate. Probably in
most circumstances they are, but ...

Cheers,
Bert

On Mon, Aug 13, 2012 at 1:15 AM, Jessica Streicher
<j.streicher@micromata.de> wrote:
> Tempting a use of let me google that for you..
>
> Anyway, theres a package called Imputation. I myself used the zoo 
package. There are probably lots of others since its a real common 
problem.
>
> They usually fill in places in you data that are designated as NA.
>
> I do not completely understand what you mean with detection limit. If 
you do not have NAs, but rather some kind of threshold, i'd suggest going 
over the data and filling any applicable values with NAs, then use the 
library of your choice. I find that kind of weird though, if you haven't 
detected much you haven't detected much. Its part of the data, why impute?
>
> On 11.08.2012, at 23:01, aynumazi wrote:
>
>> Hello,
>>
>> I'm trying to impute data below detection limit (with multiple 
detection
>> limits)
>> so i need just a method or a code for imputation and then extract the
>> complete dataset to do the analyses.
>> Is there any package which could do that simply as i'm a beginner in R
>>
>> Thank you
>>
>>
>>
>> --
>> View this message in context: 
http://r.789695.n4.nabble.com/Imputing-data-below-detection-limit-tp4640057.html

>> Sent from the R help mailing list archive at Nabble.com.
>>
>> ______________________________________________
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>
> ______________________________________________
> R-help@r-project.org mailing list
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> PLEASE do read the posting guide 
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-- 

Bert Gunter
Genentech Nonclinical Biostatistics

Internal Contact Info:
Phone: 467-7374
Website:
http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm


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